1. Field of the Invention
The present invention relates to a method for providing an estimate of the position, speed, and direction of travel of a contact or target and a system for performing said method.
2. Description of the Prior Art
Computer based technology has advanced to the point where artificial systems have been developed which simulate the operation of the human brain. These systems are known as neural networks. Typically, the systems use numerous nonlinear computational elements operating in parallel and arranged in patterns reminiscent of biological neural networks. Each computational element or neuron is connected via weights or synapses that are adapted during training to improve performance. Many of these systems exhibit self-learning by changing their synaptic weights until the correct output is achieved in response to a particular input. As a consequence, these systems have lent themselves to use in a number of different applications.
One such application is target imaging and identification systems. U.S. Pat. No. 4,995,088 to Farhat illustrates a data analysis system for such an application. Farhat's data analysis system comprises a first array for receiving input data comprising a plurality of neural elements for transmitting data signals and memory means for processing the data signals transmitted by the elements of the first array. The memory means has associatively stored therein in accordance with a Hebbian model of learning for neural networks, at least one quantized feature space classifier of a known data set. The system further comprises a second array having a plurality of neural elements for receiving the data signals processed by the memory matrix. The neural elements of the second array are operatively coupled in feedback with the neural elements of the first array wherein the neural elements of the second array provide feedback input for the neural elements of the first array. In a preferred embodiment of the Farhat system, the neural elements of the first array comprise light emitting elements and the neural elements of the second array photo-detectors.
A second application is contact state estimation. The general contact state estimation, or target motion analysis, problem is to estimate contact location and motion from all available information. This information may include available sensor readings, environmental data, contact kinematics, and historical data. A three dimensional ocean modeling method in conjunction with a data fusion technique must be employed in order to exploit all available information in ascertaining a contact's state.
In a broad sense, each sensor reading provides constraints on the contact state. If sufficient observations are available, and if assumptions are made about the contact motion (such as constant speed and heading), then the contact state may be constrained to a single solution. Due to uncertainty, or error, associated with physical sensor readings, contact state determination becomes a parameter estimation problem. Noisy sensor readings will preclude an exact solution for contact state; therefore, a method must be employed to determine the most likely state estimate.
U.S. Pat. No. 5,488,589 to DeAngelis describes a system and method for contact state estimation that incorporates three dimensional ocean modeling with a data fusion technique to exploit all available information in ascertaining a contact's state. However, in estimating the state of a moving contact, the system and method of the DeAngelis patent constrains the motion of a contact to a model in which the contact follows a course defined by a constant heading and a constant speed at a constant depth. Such a limitation reduces the accuracy of contact state estimates, particularly in an environment where a majority of the contacts are capable of travel at varying speeds, headings and depths.
Thus, what is needed is a method and device for contact state estimation that does not constrain the motion of the contact to a single model of constant motion, that does not require significant computational demands and that incorporates fusion of multiple sensor information and a-priori information.